EXCEED THE SPACE PROVIDED. The identification of the genetic determinants of complex mental illnesses such as anxiety disorders, attention deficit hyperactivity disorder, autism spectrum disorders, depression, and schizophrenia is a goal of paramount importance. In this MERIT renewal, we proposeto develop and test a battery of statistical genetic methods that are geared towards the use of endophenotypic data to localize and identify the quantitative trait loci (QTLs) related to mental illness. Our methodological approach utilizes a general variance component framework that we have been developing for a decade. While we have previously focused on QTL localization, we will also emphasize the statistical identification of causal genes and the dissection of their functional allelic architecture. Our four specific aims include: 1) the development of variance component methods for the objective selection of high dimensional quantitative endophenotypes using family-based data, 2) the extension of variance component-based QTL localization procedures to allow for such biological complexities as epigenetic effects, general models of genotype-by-environment interaction, and joint analysis of disease status with a quantitative endophenotype, 3) the development of statistical genetic methods for the identification of genes underlying QTLs and for the statistical dissection of their constituent functional variants (the QTNs), and 4) the incorporation of these new methods and features into our existing software package, SOLAR, for the genetic analysis of complex traits. Mental illnesses account for a substantial proportion of the total morbidity burden in the US. Such disorders are relatively poorly understood in terms of their underlying pathobiology. By identifying the specific genes that contribute to risk of mental illness, we will obtain information on proximate causal determinants of these diseases, which will which will provide a window into the pathobiological process and may suggest novel therapeutic approaches.